With the rapid development of three-dimensional (3D) scanning devices, it is possible to quickly digitize 3D information in the real world. The point cloud is gradually becoming an effective way to express a 3D scene and a 3D surface of an object. As compared with the traditional two-dimensional (2D) pictures and videos, the point cloud is usually distributed discretely in the 3D space in a form of points, thereby supporting a free viewpoint and a multi-angle viewing, satisfying people's user experience of the 3D world, and being applied more and more widely. The point cloud is acquired by sampling the surface of the object by the 3D scanning device. There are a large number of points, and each point contains (x, y, z) geometric information, and attribute information such as color and texture, i.e., the information amount is large, so the point cloud has a huge data amount. In consideration of the large data amount of the point cloud and the limited bandwidth of the network transmission, the point cloud compression is an imperative task.
The point cloud compression is mainly classified into a geometric compression and an attribute compression, wherein the point cloud attribute compression is an active and promising technical research field. The existing framework of the point cloud attribute compression mainly includes:
I. A method based on octree decomposition and hierarchical transformation: the method firstly uses an octree to perform a spatial decomposition of the point cloud, and then uses low-level attribute information to predict high-level attribute information based on a hierarchical structure of the octree, thereby realizing a hierarchical transformation. This method has a high processing efficiency but a poor compression performance.
II. A method based on octree decomposition and Discrete Cosine Transform (DCT): the method firstly uses an octree to perform a spatial decomposition of the point cloud to obtain a hierarchical structure from a “root node” to a “leaf node”; then carries out a depth-first traversal on the octree, and writes the traversed node color values into a 2D JPEG table in a serpentine manner; next, uses an existing JPEG encoder to encode a obtained point cloud color table, wherein the JPEG encoder adopts a DCT transform. This method uses the existing encoder and has a low computational complexity, but it does not make full use of the spatial correlation between the points and needs to be improved in the compression performance.
III. A method based on octree decomposition and graph transformation: the method firstly uses an octree to perform a spatial decomposition of the point cloud, which is divided into a certain hierarchy to obtain a transformation block, and a graph is formed in each transformation block; within the transformation block, two points with a distance not more than 1 along any coordinate axis are connected by an edge whose weight is inversely proportional to an Euclidean distance, and the points and the edge form a graph; next, a graph transformation is carried out on the attribute information of the nodes on the graph. This method has a good compression performance, but the computational complexity is relatively high, and its composition mode may bring about a sub-graph issue, which affects the efficiency of the graph transformation, so there is still room for an improvement of the compression performance.